ISA 444: Business Forecasting

.title[
# ISA 444: Business Forecasting
]
.subtitle[
## 02: Introduction to <svg viewBox="0 0 581 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"></path></svg>
]
.author[
### <br>Fadel M. Megahed, PhD <br><br> Endres Associate Professor <br> Farmer School of Business<br> Miami University<br><br> <a href="https://twitter.com/FadelMegahed"><svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"></path></svg> <span class="citation">@FadelMegahed</span></a> <br> <a href="https://github.com/fmegahed/"><svg viewBox="0 0 496 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"></path></svg> fmegahed</a> <br> <a href="mailto:fmegahed@miamioh.edu"><svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M476 3.2L12.5 270.6c-18.1 10.4-15.8 35.6 2.2 43.2L121 358.4l287.3-253.2c5.5-4.9 13.3 2.6 8.6 8.3L176 407v80.5c0 23.6 28.5 32.9 42.5 15.8L282 426l124.6 52.2c14.2 6 30.4-2.9 33-18.2l72-432C515 7.8 493.3-6.8 476 3.2z"></path></svg> fmegahed@miamioh.edu</a><br> <a href="https://calendly.com/fmegahed"><svg viewBox="0 0 384 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M202.021 0C122.202 0 70.503 32.703 29.914 91.026c-7.363 10.58-5.093 25.086 5.178 32.874l43.138 32.709c10.373 7.865 25.132 6.026 33.253-4.148 25.049-31.381 43.63-49.449 82.757-49.449 30.764 0 68.816 19.799 68.816 49.631 0 22.552-18.617 34.134-48.993 51.164-35.423 19.86-82.299 44.576-82.299 106.405V320c0 13.255 10.745 24 24 24h72.471c13.255 0 24-10.745 24-24v-5.773c0-42.86 125.268-44.645 125.268-160.627C377.504 66.256 286.902 0 202.021 0zM192 373.459c-38.196 0-69.271 31.075-69.271 69.271 0 38.195 31.075 69.27 69.271 69.27s69.271-31.075 69.271-69.271-31.075-69.27-69.271-69.27z"></path></svg> Automated Scheduler for Office Hours</a><br><br>
]
.date[
### Spring 2023
]

---

# Quick Refresher from Last Class

✅ Describe **course motivation** and **structure**.

✅ Explain the differences between **cross sectional**, **time series** and **panel** datasets.

✅ Describe the **components of time series** datasets.

✅ Explain the **forecasting steps**.

---

# Learning Objectives for Today's Class

- Describe the syntax, data types, and data structures in <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>.

- Access the help for <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> functions (each help file has the following components: Description, usage, arguments, value, and examples).

- Utilize the project workflow in <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> and create <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> script.

- Access, subset, and create `ts()` objects in <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>.

---

# Learning <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> (Any Programming Language)

* 🗣 **Get hands dirty**‼️

* 📖 Documentation! Documentation! Documentation!

* 🔎 (Not surprisingly) Learn to Google: what that error message means (I <svg aria-hidden="true" role="img" viewBox="0 0 488 512" style="height:1em;width:0.95em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M488 261.8C488 403.3 391.1 504 248 504 110.8 504 0 393.2 0 256S110.8 8 248 8c66.8 0 123 24.5 166.3 64.9l-67.5 64.9C258.5 52.6 94.3 116.6 94.3 256c0 86.5 69.1 156.6 153.7 156.6 98.2 0 135-70.4 140.8-106.9H248v-85.3h236.1c2.3 12.7 3.9 24.9 3.9 41.4z"/></svg> a lot 😂)

<hr>

</html>

**Source:** Slide is based on [Kia Ora's How I Learn a Technology](https://stats220.earo.me/01-intro.html#7).
]

---

# The RStudio Interface, Setup and a Project-Oriented Workflow for your Analysis

---

## RStudio Interface

???

live

---

## Setting up RStudio (do this once)

.pull-left[
Go to **Tools** > **Global Options**:
.center[<img src="../../figures/rstudio-setup.PNG" width="100%">]
]
.pull-right[
<br>
<br>
<br>
<br>
Uncheck `Workspace` and `History`, which helps to keep <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> working environment fresh and clean every time you switch between projects.
]

---

## What is a Project?

* Each university course is a project, and get your work organised.
* A self-contained project is a folder that contains all relevant files, for example my `ISA 444/` 📂 includes:
   + `isa444.Rproj`
   + `lectures/`  
     +  `01_introduction/`  
          *  `01-intro.Rmd`, etc.   
     +  `02_introduction_to_r/`  
          *  `02_intro_r.Rmd`, etc. 
* All working files are **relative** to the **project root** (i.e. `isa444/`).
* The project should just work on a different computer.

---

## Lets Create a `.Rproj` for Our Course

.pull-left-2[
1. Click the **Project** icon on the top right corner 
<br>
<br>
<br>
<br>
2. **New Directory**/**Existing Directory** > **New Project** > **Create Project**
<br>
<br>
<br>
3. Open the project
]
.pull-right-2[
.center[<img src="../../figures/rstudio-proj1.png" width = "100%">]
]

---

# <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> 101: Syntax, Data Types, Data Structures and Functions

---

# Coding Style

> .font150[Good coding style is like correct punctuation: you can manage without it, butitsuremakesthingseasiertoread.
 <br> -- [The tidyverse style guide](https://style.tidyverse.org)]

### <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> style guide

]
.pull-right[
❌ `camelCase` (Javascript)

❌ `PascalCase` (Python)
]

**Source:** Slide is based on [Earo Wang's STAT 220 Slides](https://stats220.earo.me/01-intro.html#34)
]

---

# Data Types: A Visual Introduction

.center[<img src="https://d33wubrfki0l68.cloudfront.net/8a3d360c80da1186b1373a0ff0ddf7803b96e20d/254c6/diagrams/vectors/atomic.png" width="60%">]

- To check the **type of** an object in <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>, you can use the function `typeof`.

.footnote[
<html>
<hr>
</html>
**Source:** The image is from [Hadley Wickham's Advanced R: Chapter 3 on Vectors](https://adv-r.hadley.nz/vectors-chap.html)
]

---
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# Data Types: A Visual Introduction

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#../../figures/legos-jbryan-types.png" alt="A visual representation of different types of atomic vectors" width="100%" />
<p class="caption">A visual representation of different types of atomic vectors</p>
</div>

.footnote[
<html>
<hr>
</html>
**Source:** The images are from the excellent [lego-rstats GitHub Repository by Jenny Bryan](https://github.com/jennybc/lego-rstats#readme)
]

---

# Data Types: Formal Definitions

Each of the four primary types has a special syntax to create an individual value:

- Logicals can be written in full (`TRUE` or `FALSE`), or abbreviated (`T` or `F`).

- Doubles can be specified in decimal (`0.1234`), scientific (`1.23e4`), or hexadecimal (`0xcafe`) form.   
    * There are three special values unique to doubles: `Inf`, `-Inf`, and `NaN` (not a number).   
    * These are special values defined by the floating point standard.

- Integers are written similarly to doubles but must be followed by `L`(`1234L`, `1e4L`, or `0xcafeL`), and can not contain fractional values.

- Strings are surrounded by `"` (e.g., `"hi"`) or `'` (e.g., `'bye'`). Special characters are escaped with `\` see `?Quotes` for full details.

.footnote[
<html>
<hr>
</html>
**Source:** The content of this slide is verbatim from [Hadley Wickham's Advanced R: Chapter 3 on Vectors](https://adv-r.hadley.nz/vectors-chap.html#scalars)
]

---

# Data Structures: Atomic Vector (1D)

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#../../figures/legos-jbryan-types.png" alt="A visual representation of different types of atomic vectors" width="100%" />
<p class="caption">Keeping the  visual representation of different types of atomic vectors in your head!!</p>
</div>

.footnote[
<html>
<hr>
</html>
**Source:** The images are from the excellent [lego-rstats GitHub Repository by Jenny Bryan](https://github.com/jennybc/lego-rstats#readme)
]

---

# Data Structures: 1D ➡️ 2D

.footnote[
<html>
<hr>
</html>
**Source:** The images are from the excellent [lego-rstats GitHub Repository by Jenny Bryan](https://github.com/jennybc/lego-rstats#readme)  
]

---

# Data Structures: Lists

An object contains elements of **different data types**.

.footnote[
<html>
<hr>
</html>
**Source:** The image is adapted from the excellent [lego-rstats GitHub Repository by Jenny Bryan](https://github.com/jennybc/lego-rstats/blob/master/lego-rstats_014.jpg)
]

---
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# Data Structures: Lists

.center[<img src="https://d33wubrfki0l68.cloudfront.net/9628eed602df6fd55d9bced4fba0a5a85d93db8a/36c16/diagrams/vectors/list.png" width="100%">]

```r
lst <- list( # list constructor/creator
* 1:3, # atomic double/numeric vector  of length = 3
* "a", # atomic character vector of length = 1 (aka scalar)
* c(TRUE, FALSE, TRUE), # atomic logical vector of length = 3
* c(2.3, 5.9) # atomic double/numeric vector of length =3
)
lst # printing the list
```

```
## [1] "1:3"                  "a"                    "c(TRUE, FALSE, TRUE)"
## [4] "c(2.3, 5.9)"
```

.footnote[
<html>
<hr>
</html>
**Source:** Image is from [Hadley Wickham's Advanced R: Chapter 3 on Vectors](https://adv-r.hadley.nz/vectors-chap.html#lists)
]

---
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# Data Structures: Lists

```r
lst[1]
```

```
## [[1]]
## [1] 1 2 3
```
]
.pull-right[
Subset by `[[]]`

```r
lst[[1]]
```

```
## [1] 1 2 3
```
]

.footnote[
<html>
<hr>
</html>
**Sources:** The slide is based on [Earo Wang's STAT 220 slides](https://stats220.earo.me/02-import-export.html#10) and image is from [Hadley Wickham's Tweet on Indexing lists in R](https://twitter.com/hadleywickham/status/643381054758363136?lang=en).
]

---

# Data Structures: Matrices

A matrix is a **2D data structure** made of **one/homogeneous data type.**

```r
x_mat = matrix( sample(1:10, size = 4), nrow = 2, ncol = 2 ) 
str(x_mat) # its structure?
```

```
##  int [1:2, 1:2] 7 5 1 6
```

```r
x_mat # printing it nicely
print('-----------------')
*x_mat[1, 2] # subsetting
```

```
##      [,1] [,2]
## [1,]    7    1
## [2,]    5    6
## [1] "-----------------"
## [1] 1
```

]

```r
x_char = matrix(
  sample(letters, size = 12), nrow = 3, ncol =4)
x_char
```

```
##      [,1] [,2] [,3] [,4]
## [1,] "d"  "e"  "a"  "u" 
## [2,] "v"  "s"  "k"  "z" 
## [3,] "b"  "n"  "l"  "t"
```

```r
*x_char[1:2, 2:3] # subsetting
```

```
##      [,1] [,2]
## [1,] "e"  "a" 
## [2,] "s"  "k"
```
]

---

# Data Structures: Data Frames

.center[<img src="https://d33wubrfki0l68.cloudfront.net/9ec5e1f8982238a413847eb5c9bbc5dcf44c9893/bc590/diagrams/vectors/summary-tree-s3-2.png" width="22%">]

> .font150[If you do data analysis in R, you’re going to be using data frames. A data frame is a named list of vectors with attributes for `(column)` `names`, `row.names`, and its class, “data.frame”.  -- [Hadley Wickham](https://adv-r.hadley.nz/vectors-chap.html#list-array)]

.footnote[
<html>
<hr>
</html>
**Source:** Image is from [Hadley Wickham's Advanced R: Chapter 3 on Vectors](https://adv-r.hadley.nz/vectors-chap.html#list-array)
]

---
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# Data Structures: Data Frames

```r
df1 <- data.frame(x = 1:3, y = letters[1:3])
typeof(df1) # showing that its a special case of a list
```

```
## [1] "list"
```

```r
attributes(df1) # but also is of class data.frame
```

```
## $names
## [1] "x" "y"
## 
## $class
## [1] "data.frame"
## 
## $row.names
## [1] 1 2 3
```

In contrast to a regular list, a data frame has **an additional constraint: the length of each of its vectors must be the same.** This gives data frames their **rectangular structure.**

.footnote[
<html>
<hr>
</html>
**Source:** Content is from [Hadley Wickham's Advanced R: Chapter 3 on Vectors](https://adv-r.hadley.nz/vectors-chap.html#list-array)
]

---
count: false
# Data Structures: Data Frames

As noted in the creation of `df1`, columns in a data frame can be of different types. Hence, it is more widely used in data analysis than matrices.

---

# Data Structures: So What is a Tibble Anyway?

> Tibble is a **modern reimagining of the data frame**. Tibbles are designed to be (as much as possible) **drop-in replacements for data frames** that fix those frustrations. A concise, and fun, way to summarise the main differences is that tibbles are **lazy and surly: they do less and complain more**. -- [Hadley Wickham](https://adv-r.hadley.nz/vectors-chap.html#list-array)

.pull-left[[<img src="https://d33wubrfki0l68.cloudfront.net/565916198b0be51bf88b36f94b80c7ea67cafe7c/7f70b/cover.png" height="320px">](https://adv-r.hadley.nz)]

To learn more about the basics of tibble, please consult the reference below: 
* [Data frames and tibbles (Click and read from 3.6 up to and including 3.6.5)](https://adv-r.hadley.nz/vectors-chap.html#list-array)

---

# Functions

A function call consists of the **function name** followed by one or more **argument** within parentheses.

```r
temp_high_forecast = c(34, 36, 41, 44, 27, 32, 35)
mean(x = temp_high_forecast)
```

```
## [1] 35.57143
```

* function name: `mean()`, a built-in R function to compute mean of a vector
* argument: the first argument (LHS `x`) to specify the data (RHS `temp_high_forecast`)

**Source:** Slide is based on [Earo Wang's STAT 220 Slides](https://stats220.earo.me/01-intro.html#41)
]

---

# Function Help Page

Check the function's help page with `?mean`

### Class Activity

> _Please take 2 minutes to investigate the help page for `mean` in R Studio._

```r
mean(x = temp_high_forecast, trim = 0, na.rm = FALSE, ...)
```

* Read **Usage** section
  + What arguments have default values?
* Read **Arguments** section
  + What does `trim` do?
* Run **Example** code

**Source:** Slide is based on [Earo Wang's STAT 220 Slides](https://stats220.earo.me/01-intro.html#42)
]

---

# Function Arguments

```r
mean(temp_high_forecast, 0.1, TRUE)
```

```
## [1] 35.57143
```
]

```r
mean(x = temp_high_forecast, trim = 0.1, na.rm = TRUE)
```

```
## [1] 35.57143
```
]

**Source:** Slide is based on [Earo Wang's STAT 220 Slides](https://stats220.earo.me/01-intro.html#43)
]

---

# Use Functions from Packages

```r
library(dplyr)
dplyr::cummean(temp_high_forecast)
```

```
## [1] 34.00000 35.00000 37.00000 38.75000 36.40000 35.66667 35.57143
```

```r
dplyr::first(temp_high_forecast)
```

```
## [1] 34
```

```r
dplyr::last(temp_high_forecast)
```

```
## [1] 35
```
]
.pull-right[
<br>
<br>
<br>
<br>
.center[
<img src="https://raw.githubusercontent.com/STATS-UOA/stats220/master/lectures/img/install-library.JPG" height="240px">
]
]

**Source:** Slide is based on [Earo Wang's STAT 220 Slides](https://stats220.earo.me/01-intro.html#44)
]

---
class: inverse, center, middle

---

# The *ts* Object

---

# Create a *ts* Object by asking *ChatGPT*

---

# Demo 1: `Nile` & `AirPassengers` Data

In class, we will create our first <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> script where we will examine one of these two built-in datasets. In our exploration, we will:

- Examine the `typeof()` the dataset.

- Examine the `class()` of the dataset.

- Examine the `length()` of the dataset.

- `print()` the data set and examine its `frequency()`.

- Subset the data using `window()` and non ts-based sub-setting techniques.  
  + Useful for the concept of the entire time series vs a snippet that we discussed in [Class 01]().

---

# A Poll: File Paths in R

---

# Demo 2: Loading Time Series Data into R

- Reading CSV Files through either the `read.csv()` or `readr::read_csv()` functions.

- Reading FRED and Yahoo Finance Data using the `tidyquant` package.

- Preprocessing and converting the data into a time series object.

## Bonus Tricks (if time allows)

- `file.choose()` -- not typically introduced by Miami faculty!!

- The datapasta package <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:gold;overflow:visible;position:relative;"><path d="M50.7 58.5L0 160H208V32H93.7C75.5 32 58.9 42.3 50.7 58.5zM240 160H448L397.3 58.5C389.1 42.3 372.5 32 354.3 32H240V160zm208 32H0V416c0 35.3 28.7 64 64 64H384c35.3 0 64-28.7 64-64V192z"/></svg>

---

# Summary of Main Points

By now, you should be able to do the following:

---

# Things to Do to Prepare for Our Next Class

- Go over your notes, read the **references below**, and **complete** the [self-paced R tutorial](http://rstudio.fsb.miamioh.edu:3838/megahefm/isa444/spring2023/datatypes/).

- Complete [Assignment 02](https://miamioh.instructure.com/courses/188655/assignments/2368786) on Canvas.

.pull-left[
.center[[<img src="https://d33wubrfki0l68.cloudfront.net/b88ef926a004b0fce72b2526b0b5c4413666a4cb/24a30/cover.png" height="350px">](https://r4ds.had.co.nz)]
]
.pull-right[

* [Data Visualization](https://r4ds.had.co.nz/data-visualisation.html)

* [Graphics for Communication](https://r4ds.had.co.nz/graphics-for-communication.html)

* [Dates and Times](https://r4ds.had.co.nz/dates-and-times.html)

]